Issue 69

A. Anjum et alii, Frattura ed Integrità Strutturale, 69 (2024) 43-59; DOI: 10.3221/IGF-ESIS.69.04

ML algorithms, such as ANN models, were utilized to predict the residual flexural strength of corroded RC beams [38]. ANN models were also applied to compute reduction factors to estimate the moment of inertia effectiveness in shape memory alloy RC beams [39]. To expedite the accurate design of RC columns and bridge piers, various ML-based functions were introduced. ANNs, in combination with well-constructed extensive training sets, can produce models with design accuracy that surpass traditional design charts and approach iterative section analysis techniques. This technique outperforms conventional design algorithms while maintaining stability, as demonstrated in a computational performance comparison [40].

Figure 1. Classification of Algorithms for Statistical Model Development in SHM.

An ML method was implemented for automated crack detection in a concrete bridge, using a robust multifeatured classifier tailored for spatial adjustments. Results were displayed using a robotic bridge scanning system, effectively locating potential break areas even in noisy conditions and computing spatially customized visual characteristics [41]. Accurate anticipation of hygrothermal activity in concrete is crucial to making informed service-life extension decisions. An ANN-based hygrothermal prediction model was developed for assessing the temporal hygrothermal state in surface-protected concrete façade components [42]. Furthermore, ML techniques were employed to predict the shear strength and behavior of RC beams reinforced with externally bonded FRP sheets [43]. Existing shear design models for FRP-reinforced concrete structures tend to be overly conservative, increasing construction costs. An updated teeth model accounting for FRP reinforcement was optimized using a genetic algorithm and a database of longitudinally strengthened thin beams with FRP rebars, yielding a more accurate shear equation [44]. A model-free damage detection system based on ML approaches was also created for a simulated railway concrete bridge using a three-dimensional FE model [45]. This method analyses data collected from a structure in different states through a piezoelectric sensor network [46]. In the literature, an ML-based backbone curve model for RC columns subjected to cyclic loading reversals was found and utilized for predicting RC column strength [47]. ML was also employed for fracture mode categorization based on unlabelled acoustic emission waveform characteristics [48]. Additionally, when combined with unmanned aerial vehicles, an ML-based model enhanced the automation level of concrete infrastructure inspection by detecting surface fractures. This was achieved using a deep learning convolutional neural network (CNN) image classification technique to create the crack detection model [49]. Various crack models are illustrated in Fig. 2. It was employed in a regression model for crack development and propagation to validate ML's applicability using inspection data from a concrete bridge. This process involves algorithm selection, innovative model development, and data analysis [50]. ML was also used to assess the condition rating of a concrete bridge through the analysis of impact echo data [51].

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